Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Neural Networks via Complex Network Theory: A Perspective
Authors: Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia, Vito Latora
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to assess to which extent CNT identifies patterns in DNNs: we define three complementary levels of analysis. The first level (I) aims to distinguish dominating CNT patterns for architecturally similar networks: we train on MNIST and CIFAR10 three-layer depth FCs, CNNs, RNNs and AEs equipped with the same activation functions and a comparable number of parameters. |
| Researcher Affiliation | Academia | 1University of Oxford 2King s College London 3University of Catania 4Queen Mary University of London |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Results for the other architectures on MNIST and CIFAR10, for five and nine layers, are reported in the code repository. |
| Open Datasets | Yes | We conduct all the experiments on two standard datasets in pattern recognition and computer vision, namely MNIST and CIFAR10 [Lecun and Bengio, 1995; Krizhevsky et al., 2010]. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR10 datasets but does not provide specific train/validation/test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We initialise the weights of each DNN via sampling from a Gaussian distribution of known variance between 0.05 (MNIST) and 0.5 (CIFAR10).Results for the other architectures on MNIST and CIFAR10, for five and nine layers, are reported in the code repository. |